An AI-Enhanced IoT System for Soilless Agriculture: Environmental Monitoring and Market-Driven Decision Support Integration

This study presents an AI-enhanced IoT system designed for soilless agriculture, aiming to monitor environmental conditions, automate climate control, and optimize production decisions based on market data. The system is built on the Deneyap microcontroller and is equipped with sensors that measure temperature, humidity, light intensity, water level, and air quality. Sensor data are transmitted in real time to a mobile application through a cloud-based database service, enabling both manual and automatic operation modes. In automatic mode, environmental parameters are compared to predefined thresholds to activate fan, pump, and lighting systems. Additionally, a machine learning model based on the Random Forest Regressor algorithm was developed to analyze market demand, price fluctuations, and seasonal trends. Trained on wholesale price data from Izmir Municipality (2019–2022), the model provides price forecasts for selected products on specific dates predictive insights for various product-date combinations and contributes to production planning. The integrated approach of environmental monitoring and market-aware decision-making demonstrates a novel, scalable, and practical solution for smart agriculture practices.